Identifying operability failure in dr assets

ABSTRACT

The present invention demonstrates a highly distributed demand response optimization and management system for real-time (DROMS-RT) power flow control to support large scale integration of distributed renewable generation into the grid. The system is a cloud-based platform that reduces critical peak power safely and securely. The arrangement is provided with a control and communications platform to allow highly dispatchable demand response (DR) services in timeframes suitable for providing ancillary services to the transmission grid. The services are substantially more efficient than other forms of ancillary service options currently available to manage the intermittency associated with large-scale renewable integration.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 61/726,023, filed Nov. 14, 2012, the disclosure of which is herebyincorporated by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates generally to demand response (DR)measurement and verification (M&V) and demand response (DR) deviceoperability analysis; and more particularly to statistical algorithmsapplied to automatic detection of mechanical failures in dispatchabledemand response assets.

BACKGROUND OF THE INVENTION

Demand response (DR) is a mechanism to manage customer's consumption ofelectricity in response to supply conditions, for example, havingelectricity customers reduce their consumption at critical times or inresponse to market prices. Demand response is generally used toencourage consumers to reduce demand, thereby reducing the peak demandfor electricity. Demand response gives the consumers the ability tovoluntarily trim or reduce their electricity usage at specific times ofthe day during high electricity prices, or during emergencies.

In other words, demand response is a resource that allows end-useelectricity customers to reduce their electricity usage in a given timeperiod, or shift that usage to another time period, in response to aprice signal, a financial incentive, an environmental condition, or areliability signal. Demand response saves ratepayer's money by loweringpeak time energy usage that is high-priced. This lowers the price ofwholesale energy, and in turn, retail rates. Demand response may alsoprevent rolling blackouts by offsetting the need for more electricitygeneration and can mitigate generator market power.

There are two general classes of DR program—dispatchable andnon-dispatchable. Non-dispatchable DR is not dependent on explicitsignaling from the load serving entity (LSE) to the participant.Examples of non-dispatchable DR include time-of-use (TOU) and real timepricing (RTP) programs. In contrast, dispatchable demand responseentails the load serving entity sending explicit signals to participantsto reduce energy consumption during a specified time window. The signalscan either communicate economic incentives to participants or controlparticipant devices directly. An example of the former is critical peakprice (CPP) demand response programs, while the latter are referred toas direct load control (DLC) demand response.

For both types of dispatchable demand response there are switches andsignal receptors at the participant site that must function properly forthe desired load curtailment to be realized. An important issue facingload serving entities is that as large numbers of these devices fail,the overall effectiveness of the demand response (DR) program isseverely degraded. Load serving entities can perform physical on-siteinspections to monitor and fix broken devices, but this approach isextremely expensive to scale up to many thousands of participants if theinspections are not well-targeted to those sites with a high probabilityof device failure. Alternatively, waiting for participants toself-report a failed device before visiting a site can result inconsistently poor demand response performance over the course of theprogram.

Given the above, there is a huge value in a system that can analyzereal-time and historical data on demand response event performance andautomatically identify those participants that have a high-probabilityof device failure. The demand response optimization and managementsystem for real time system contains an operability analysis engine(OAE) that employs advanced, customized statistical methods to generatea probability that a given participant has a device failure based onhistorical interval meter data.

SUMMARY OF THE INVENTION

The present invention describes a highly distributed demand responseoptimization and management system. The system utilizes a scalable,web-based software-as-a-service platform that provides all programdesigns, implementation and execution, event management, forecasting,optimal dispatch and post-event analytics functionality. The system usesadvance machine learning and robust optimization techniques forreal-time and “personalized” demand response-offer dispatch. Thesystem's learning engine keeps track of available demand side resourcesand history of participation in different demand response events atindividual customer locations. This data is used to build a virtualprofile for each customer that is able to forecast the amount ofload-shed, shed-duration, and rebound effects for that customer giventhe time-of-day, weather and price signal. The virtual profiles for thecustomers are generated in an online manner and are continuously updatedin real-time as more usage data becomes available.

The embodiment of the present invention provides a demand responseoptimization and management system for real-time that comprises autility data feed, a resource modeler, a forecasting engine, anoptimization engine comprising an optimizer and a dispatch engine, abaseline engine, a customer/utility interface and a customer data feed.The arrangement described herein is provided with a control andcommunications platform to allow highly dispatchable demand response(DR) services in timeframes suitable for providing ancillary services tothe transmission grid. The services are substantially more efficientthan other forms of ancillary services options currently available tomanage the intermittency associated with large-scale renewableintegration.

The present invention identifies operability failures in demand responseassets by employing a customized 2-stage statistical method as a mixturemodel stage and a Bayesian update stage. The two customized stagesgenerate the probability of a device failure for each demand responseparticipant based on historical meter data and participant baseline load(PBL) estimates produced by an operability analysis engine (OAE). Demandresponse optimization and management system for real-time leverage thelow-cost, open, interoperable demand response signaling technology, openautomated demand response (OpenADR), and internet-protocol basedtelemetry solutions to reduce the cost of hardware, allowing DROMS-RT toprovide dynamic price signals to millions of open automated demandresponse clients. The present invention described herein has itsimplementation in the demand response system; however it does not limitthe scope of the described invention.

ABBREVIATIONS AND DEFINITIONS

Demand Response Optimization and Management System for Real-Time(DROMS-RT): DROMS-RT is a highly distributed demand responseoptimization and management system for real-time power flow control tosupport large scale integration of distributed generation into the grid.

Demand Response (DR): Demand response is a mechanism to manage customerconsumption of electricity in response to supply conditions. DR isgenerally used to encourage consumers to reduce demand, thereby reducingthe peak demand for electricity.

Forecasting Engine (FE): The forecasting engine generates short-termforecasts of participant-level baseline load (BL).

Operability Analysis Engine (OAE): The operability analysis enginegenerates the probability that a given participant has a device failure,based on historical DR event results.

Participant Baseline Load (PBL): The estimated non-curtailed load of aparticipant over a period of time; that is, the load that would havehappened had a DR event not been called for the time interval inquestion.

Load Serving Entities (LSE): A load-serving entity secures energy,transmission and related interconnected operations services, to servethe electrical demand and energy requirements of its end-use customers.

Measurement and Verification (M&V): Measurement and verification (M&V)is the independent analysis and reporting of demand side management andenergy efficiency (DSMEE) saving impacts.

Real Time Pricing (RTP): Real time pricing means tarrifed retail chargesfor delivered electrical power and energy that vary hour-to-hour.

Critical-Peak Pricing (CPP): Critical-peak pricing is a pricingtechnique that helps to reduce electricity bill and it can be used inperiods of high electricity demand.

Direct Load Control (DLC): Direct load control allows a utility to turnon and off specific appliances during peak demand periods.

Time Of Use (TOU): Time of use means that the electricity prices are setfor a particular time period on an advance basis.

Cumulative Distribution Function (CDF): Cumulative distributive functiongives the probability that a random variable is less than or equal tothe independent variable of the function.

Expectation-Maximization (EM) algorithm: EM algorithm is an iterativemethod for finding maximum likelihood estimates of parameters instatistical models, where the model depends on unobserved latentvariables.

Failure Detection Engine (FDE): Failure detection engine is used forautomatic detection of mechanical failures in dispatchable DR assets.

Baseline Load (BL): Baseline load is a key to accurately assess the loadimpacts from certain types of demand response programs, particularlythose that pay directly for load reductions.

BRIEF DESCRIPTION OF THE DRAWINGS

The preferred embodiment of the invention will hereinafter be describedin conjunction with the appended drawings provided to illustrate and notto limit the scope of the invention, wherein like designation denotelike element and in which:

FIG. 1 is a schematic representation of the demand response optimizationand management system for real-time, in accordance with an embodiment ofthe present invention.

FIG. 2 is a block diagram illustrating the inputs and outputs of theoperability analysis engine, in accordance with an embodiment of thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description of the embodiments of theinvention, numerous specific details are set forth in order to provide athorough understanding of the embodiments of the invention. However, itwill be obvious to a person skilled in the art that the embodiments ofthe invention may be practiced without these specific details. In otherinstances well known methods, procedures and components have not beendescribed in details so as not to unnecessarily obscure aspects of theembodiments of the invention.

Furthermore, it will be clear that the invention is not limited to theseembodiments only. Numerous modifications, changes, variation,substitutions and equivalents will be apparent to those skilled in theart without parting from the spirit and scope of the invention.

Referring to FIG. 1, a demand response optimization and managementsystem for real-time comprises a utility data feed 101, a resourcemodeler 102, a forecasting engine 103, an optimization engine 104, abaseline engine 105, a customer/utility interface 106 and a customerdata feed 107. The demand response optimization and management systemfor real-time connects to the utility's backend data system 101 on oneside and customer end-points 108 on the other side. The demand responseoptimization and management system receives live data-feeds from eitheror both the customer data feed 107 and the utility data feed 101 tocalibrate the forecasting and optimization functions of DROMS-RT, duringthe execution of a demand response event. However, some of the feeds maynot be available all the time or in real-time; the forecasting engine103 is able to run in an “off-line” manner or with partial data feeds inthese cases.

The demand response resource modeler 102 within demand responseoptimization and management system for real-time keeps track of all theavailable demand response resources, their types, their locations andother relevant characteristics such as response times, ramp-times etc.The resource modeler 102 continuously updates the availability ofresources affected by commitment to or completion of an event andmonitors the constraints associated with each resource such as thenotification time requirements, number of events in a particular periodof time, number of consecutive events and user preferences to determinea “loading order” as to which resources are more desirable forparticipation in demand response events from a customer's perspectiveand the contract terms the price at which a resource is willing toparticipate in an event. The resource modeler 102 gets data feed fromthe client to determine if the client is “online” (i.e. available as aresource) and whether the client has opted-out of the event.

The forecasting engine 103 gets the list of available resources from theresource modelers 102. The forecasting engine 103 performs short-termforecasts of aggregate load and available load-sheds for individualloads connected to the demand response optimization and managementsystem for real-time. The forecasting engine 103 accounts for a numberof explicit and implicit parameters and applies machine learning (ML)techniques to derive short-term load and shed forecasts as well as errordistributions associated with these forecasts. The estimation of errordistribution improves the overall robustness of optimization and helpsto separate small load sheds during the events. The forecasting engine103 gets continuous feedback from the client devices through thebaseline engine 106 and increases its forecasting ability as more databecomes available to the system.

The optimization engine 104 comprises an optimizer and a dispatchengine. The optimization engine 104 takes the available resources andall the constraints from the resource modeler 102 and the forecasts ofindividual loads and load-sheds and error distributions from theforecasting engine 103 to determine the optimal dispatch of demandresponse under a given cost function. The optimization engine 104incorporates a variety of cost functions such as cost, reliability,loading order preference, GHG or their weighted sum and makes an optimaldispatch decision over a given time-horizon that could cover day-aheadand near real-time horizons simultaneously. The optimization engine 104is able to automatically select the mix of demand response resourcesbest suited to meet the needs of the grid such as peak load management,real-time balancing, regulation and other ancillary services.

The baseline engine 105 verifies whether a set of customers have mettheir contractual obligation in terms of load-sheds. The baseline engine105 uses signal processing techniques to identify even small systematicload sheds in the background of very large base signals. The forecastingengine 103 provides baseline samples and the error distribution to thebaseline engine 105. In addition, the baseline engine 105 gets the datafeed from the meter which is the actual power consumption data. Thebaseline engine 105 uses ‘event detection’ algorithm to determinewhether the load actually participated in the demand response event, andif so, what the demand reduction due to this event was. The baselineengine 105 feeds data back to the forecasting engine 103 so that itcould be used to improve the baseline forecast.

Referring to FIG. 2, the present invention 200 identifies likelyoperability failures in demand response assets by employing a customized2-stage statistical method that generates the probability of a devicefailure 207 for each demand response participant based on historicalmeter data 203 and participant baseline load (PBL) estimates 204produced by the operability analysis engine (OAE) 200.

As a pre-requisite for running the operability analysis engine (OAE),the demand response optimization and management system for real-timecollects and stores meter interval data 203 from utility data feed 101and customer data feed 107 through a large-scale data storage technologysuch as Hadoop/Hbase. In addition, the forecasting engine (FE) 103generates participant baseline load values 204 from the data availablein the baseline engine 105 for each historical time interval. Thesevalues are estimates of what the load would have been in the absence ofany demand response event. For each (DR event, participant) combinationin the historical data, the operability analysis engine 200 generates adataset comprising the following quantities:

$\begin{matrix}{{x_{i,j} = {{observed}\mspace{14mu} {percentage}{\mspace{11mu} \;}{load}\mspace{14mu} {reduction}\mspace{14mu} {for}\mspace{14mu} {event}\mspace{14mu} i}},{{participant}\mspace{14mu} j}} \\{= {{load\_ shed}/{baseline}}} \\{= {\left( {b_{i,j} - a_{i,j}} \right)/b_{i,j}}}\end{matrix}$

Where,

b_(i,j)=estimate of baseline load produced by the forecasting enginea_(i,j)=actual realized loadThe goal of the operability analysis engine 200 is to infer from thedataset of x_(i,j)'s the probability of device failure for eachparticipant. It does this in two steps: Firstly, fit a mixture model 205to the dataset X comprising two populations, one corresponding toparticipants with fully operable devices (population A), the othercorresponding to those with non-operable devices (population B). Outputsof this step are:w_(A)=proportion of all participants that are in A;w_(B)=(1−w_(A))=proportion of all participants that are in B;F_(A)(x)=cumulative distribution function (c.d.f.) for population A; andF_(B)(x)=cumulative distribution function (c.d.f) for population B.Secondly, for each participant j, compute Bayesian updates 206 of theprobability that j has a non-operable device, given a set of realizedevent outcomes x_(j).

This two-step approach allows for different choices of distribution inthe mixture model step, as well as different definitions of realizedevent outcomes in the Bayesian update step. In one instance of theinvention the dataset can be modeled as a mixture of two lognormaldistributions. The lognormal distribution enables observed loads forpopulation A (participants with operable devices) to be expressed as amultiplicative set of factors:

observed_load_(i,j) =b _(i,j) *e _(i,j) *S*z _(i,j)

Where,

b_(i,j)=estimate of baseline load produced by the forecasting engine forevent i, participant je_(i,j)=log-normal noise term that captures baseline error, mean=1S=average load shed multiplier for the populationz_(i,j)=log-normal noise term that captures variance in load shed,mean=1The observed percentage load shed is therefore:

$\begin{matrix}{x_{i,j} = {{observed\_ load}_{i,j}/b_{i,j}}} \\{= {e_{i,j}*S*z_{i,j}s}}\end{matrix}$

Taking logs produces:

log(x _(i,j))=e′ _(i,j) +S′+z′ _(i,j)

In this expression, e′ and z′ are noise terms with mean 0 and var^(e)and var^(z), respectively, and S′ is the natural log of the average loadshed multiplier S. Under this model, the log of the observed load shedsfor participants with operable devices is normally distributed with meanS′, and variance var^(e)+var^(z).

For participants in population B (i.e. those with non-operable devices),there is no demand response induced load shed by definition, so themodel reduces to:

observed_load_(i,j) =b _(i,j) *e _(i,j)

Where,

x _(i,j)=observed_load_(i,j) /b _(i,j)

x_(i,j)=e_(i,j)

log(x _(i,j))=e′ _(i,j)

The log of the observed load sheds for population B participants isnormally distributed with mean 0 and variance var^(e). Note that thepopulation A participants have two sources of variance—baseline errorand load shed variability—while population B participants only have thefirst source of variance. With the log-normal assumption, the loggeddata can therefore be modeled as a mixture of two normal distributions:Population A˜N(S′, var^(e)+var^(z))Population B˜N(0, var^(e))

Note that in this formulation there are only 3 unknowns that need to beestimated—S′, var^(e) and var^(z). The operability analysis engineestimates these 3 quantities, plus the weighting proportion w_(A), usingthe well-known expectation-maximization (EM) algorithm. Once S′, var^(e)and var^(z) are estimated, the log-normal c.d.f.'s F_(A)(x) and F_(B)(x)are derived.

In a specific instance of the invention, the Bayesian update step of themethod is implemented using parameters (both prior and conditionalprobabilities) derived from the mixture model:

-   -   Use F_(A)(0) and F_(B)(0) to calculate the probabilities p^(A)        and p^(B), the probability of negative load shed for population        A and B, respectively.    -   Calculate the probability of observing x negative load sheds out        of N events using the binomial distribution with probability p        and sample size N.    -   Given a participant's observed number of positive load sheds        after N events, calculate the probability of being in population        B (i.e. having a non-operable device) using Bayes' Rule:

p(B|x)=p(x|B)*p(B)/[p(A)*p(x|A)+p(B)*p(x|B)]

Where,

P(x|B)=binom(p^(B), N)p(x|A)=binom(p^(A), N)

$\begin{matrix}{{p(A)} = {{the}\mspace{14mu} {weighting}\mspace{14mu} {factor}\mspace{14mu} {from}\mspace{14mu} {the}\mspace{14mu} {mixture}\mspace{14mu} {model}}} \\{= w_{A}}\end{matrix}$ p(B) = (1 − w_(A))

Demand response optimization and management system for real-timegenerates a report of the updated probabilities p(B) associated witheach participant after each demand response event. Users can then takeappropriate remedial action for those participants with high probabilityof device failure, thereby greatly increasing the efficacy ofdispatchable demand response programs.

The advantages of the present invention are implementation of a systemfor automatic detection of mechanical failures in dispatchable demandresponse assets that results in reduced cost and increase in demandresponse performance during the course of the program.

We claim:
 1. A method for calculating the probability of a device to bein a non-operable state for a consumer in a demand response system usingcomputer executing instructions comprising: collecting meter intervaldata of the consumer using a utility data feed and a customer data feedand generating a data set having two subsets, a first subset having apopulation with operable device and a second subset having a populationwith non-operable device; calculating a cumulative distribution functionfor the population in the first subset and for the population in thesecond subset; and calculating a probability of the consumer having anon-operable device by computing Bayesian updates for each of theconsumer in the first subset and the second subset; wherein saidprobability is calculated on the set of realized event outcomes.
 2. Themethod of claim 1 wherein the utility data feed and the customer datafee are captured using a large scale data storage technology.
 3. Themethod of claim 1 wherein the Bayesian update is performed on thelognormal function of the cumulative distribution function of thepopulation in the first subset and the second subset.
 4. The method ofclaim 1 wherein the Bayesian update is used to calculate the probabilityof a device in the population of the second subset.
 5. A method forderiving the probability of a device to be in non-operable state in adataset of a plurality of consumers in a demand response eventcomprising: collecting meter interval data of each of the consumer usingutility data feed and customer data feed; segmenting the dataset in twosubsets: a first subset having a population with operable device and asecond subset having a population with non-operable device; expressingthe dataset as a mixture of lognormal distribution of the first subsetand the second subset to enable the observed load to be expressed asmultiplicative set of factors; calculating the variance value associatedwith the first subset and the second subset, and the weighing proportionof the first subset and the second subset; deriving the log normaldistribution of cumulative distribution function for the first and thesecond subset; and performing Bayesian update on the lognormaldistribution of cumulative distribution function for the first subsetand the second subset to derive the probability of the device to be innon-operable state.
 6. The method of claim 5 wherein the set of factorscomprises estimate of baseline load generated by forecasting engine,noise that captures baseline error, noise that capture variance in loadshed and average load shed multiplier for the population.
 7. The methodof claim 5 wherein the variance value comprises variance associated withthe noise error regard to baseline and the load shed variability.
 8. Themethod of claim 5 wherein the dataset is expressed as a mixture of twonormal distribution of the population in the first subset and the secondsubset.
 9. The method of claim 5 wherein the variance associates withthe first subset and the second subset and the weighing proportion iscalculated by expectation-maximization algorithm.
 10. The method ofclaim 5 wherein the cumulative distribution function is used tocalculate the probability of negative load shed for the first subset andthe second subset.
 11. The method of claim 5 wherein the Bayesian updatefunction is used to calculate the probability of device to be innon-operable step by observing the number of positive load sheds for anumber of events.
 12. A method for calculating the probability of anon-operable device for a consumer in a demand response system usingcomputer executing instructions comprising: collecting meter intervaldata of the consumer using utility data feed and customer data feed andgenerating a data set X having two subsets, a first subset having apopulation with operable device and a second subset having a populationwith non-operable device; generating a participant baseline loadestimate value and calculating observed percentage load shed for each ofthe consumer; calculating cumulative distribution function for thepopulation in the first subset and for the population in the secondsubset; calculating the probability of negative load shed for populationin the first subset and the second subset; calculating the probabilityof observing x negative load sheds out of N events using the binomialdistribution with probability p and sample size N; and using theconsumer's observed number of positive load sheds after N event,calculate the probability of consumers to be in second subset.
 13. Themethod of claim 12 wherein the baseline load estimate value is generatedby the forecasting engine.
 14. The method of claim 12 wherein theutility data feed and the customer data feed is collected using a largestorage database.
 15. The method of claim 12 wherein the dataset isexpressed as a mixture of lognormal function of the population in thefirst subset and the second subset.